Few-shot classification is a challenging machine learning problem and researchers have explored the idea of learning to learn or meta-learning to improve the quick adaptation ability to alleviate the few-shot challenge.
One of the most general algorithms for meta-learning is the optimization-based algorithm. [1]} and [2]} proposed to learn how to optimize the gradient descent procedure so that the learner can have a good initialization, update direction, and learning rate.
For the classification problem, researchers proposed simple but effective algorithms based on metric learning. MatchingNet [3]} and ProtoNet [4]}
learned to classify samples by comparing the distance to the representatives of each class. Our distribution calibration and feature sampling procedure does not include any learnable parameters and the classifier is trained in a traditional supervised learning way.
